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. 2025 Aug 17;11(3):e70143. doi: 10.1002/trc2.70143

The plasma p‐tau217/BD‐tau ratio improves biomarker short‐term variability in memory clinic patients

Frederikke Kragh Clemmensen 1,✉, Fernando Gonzalez‐Ortiz 2, Mathias Holsey Gramkow 1, Cristano Santos 2, Henrik Zetterberg 2,3,4,5,6,7, Kaj Blennow 2,3,8,9, Steen Gregers Hasselbalch 1,10, Kristian Steen Frederiksen 1,10, Anja Hviid Simonsen 1
PMCID: PMC12358005  PMID: 40827127

Abstract

INTRODUCTION

Assessment of short‐term intra‐ and inter‐individual variability for Alzheimer's disease (AD) plasma biomarkers is essential for clinically relevant interpretation of biomarker levels. We hypothesized that the variability of plasma tau phosphorylated at threonine 217 (p‐tau217) could be reduced by combining it with a tau marker, plasma brain‐derived tau (BD‐tau), as the p‐tau217/BD‐tau ratio.

METHODS

Three consecutive blood samples were collected from memory clinic patients within 36 days. Patients were dichotomized by cerebrospinal fluid (CSF) amyloidosis (Aβ+ = 29, Aβ− = 18). We compared intra‐ and inter‐individual variability (coefficient of variation [CV]) in the plasma p‐tau217/BD‐tau ratio with p‐tau217 alone and tested if kidney function, glycated hemoglobin, and body mass index (BMI) affected the variability. Finally, we compared the p‐tau217/BD‐tau ratio with CSF p‐tau217.

RESULTS

We found that for Aβ+ individuals, the intra‐individual variability of the plasma p‐tau217/BD‐tau ratio (CV 7.1% [95% confidence interval {CI} 5.6;8.4]) was lower than for p‐tau217 alone (CV 9.4% [95% CI 7.4;11.5]). At the group level, the variability in the p‐tau217/BD‐tau ratio was reduced in both Aβ+ (CV 15.1% [95% CI 11.7;18.7]) and Aβ− (CV 18.4% [95% CI 13.0;23.8]) individuals compared to p‐tau217 alone (Aβ+ CV 19.1 [15.0;23.4], Aβ− 27.1 [18.4;36.0]). Adjusting for estimated glomerular filtration rate, hemoglobin A1C, and BMI further reduced the inter‐individual variability of p‐tau217/BD‐tau in the Aβ+ group. CSF p‐tau217 showed higher correlation with plasma p‐tau217/BD‐tau (rho = 0.53, p = 0.0005) than with p‐tau217 alone (rho = 0.37, p = 0.02).

DISCUSSION

Our findings suggest that using the ratio of plasma p‐tau217 to plasma BD‐tau and accounting for the influence of peripheral confounders improves biomarker stability, which is important for the interpretation of longitudinal biomarker changes and to prevent misclassification.

HIGHLIGHTS

  • The plasma p‐tau217/BD‐tau ratio lowered short‐term intra‐ and, especially, inter‐individual variability compared to the variability in plasma p‐tau217 alone.

  • Plasma BD‐tau did not correlate with eGFR, HbA1c, or BMI, while plasma p‐tau217 was significantly negatively associated with BMI.

  • Adjusting for eGFR, HbA1c, and BMI further reduced the inter‐individual variability of p‐tau217/BD‐tau.

  • Additionally, CSF p‐tau217 correlated better with plasma p‐tau217/BD‐tau than with p‐tau217 alone.

Keywords: p‐tau217, brain‐derived tau, within‐subject variability, p‐tau217/BD‐tau ratio, short‐term variability, between‐subject variability, plasma biomarker, Alzheimer's disease

1. BACKGROUND

Blood‐based biomarkers (BBMs) have proven useful in research settings in identifying amyloid and tau pathology in the brain. Current research has indicated plasma phosphorylated tau at threonine 217 (p‐tau217) as a valid biomarker associated with brain amyloidosis in Alzheimer's disease (AD). 1 , 2 With a view to the clinical implementation of AD BBMs, knowledge of the markers' variability, within‐ and between‐subject (i.e., intra‐ and inter‐individual variability) is essential for the correct interpretation of biomarker changes as clinically meaningful. 3 Biomarker variability can be caused by biological variation, assay variability, comorbidities, disease‐related factors (e.g., altered biomarker production and blood–brain barrier changes), and peripheral influences and comorbidities like glycated hemoglobin, body mass index (BMI), and kidney function, which affect clearance mechanisms. 3 , 4 , 5 , 6 , 7 , 8 , 9

Brain‐derived tau is, as the name implies, tau that originates exclusively from neurons in the central nervous system, targeting the six tau isoforms in the brain. The assay excludes tau produced peripherally, for example in the liver, kidneys, or heart, that contains amino acids from an extra exon compared with tau originating from the brain. 10 This is opposed to plasma T‐tau, which is influenced by peripherally produced tau and has been shown to be weakly correlated with CSF T‐tau. 11 , 12 , 13 In this study, we examine whether the intra‐ and inter‐variability of p‐tau217 could be reduced by combining it with a brain‐derived tau marker, BD‐tau.

Plasma BD‐tau is an analytically robust marker 14 that may ensure more accurate assessment of brain tau, as it has shown a strong correlation with CSF T‐tau 10 and with plasma p‐tau217. 15 Studies have investigated blood BD‐tau as a promising marker of neurodegenerative intensity in AD and rapidly progressive dementias, such as Creutzfeldt–Jakob disease (CJD) 16 , 17 , 18 and as a severity marker in stroke 19 , 20 and severe traumatic brain injury. 21

Combining markers in ratios may reduce biomarker variability, which may confound biomarker performance and interpretation. A key example is the CSF amyloid beta (Aβ) 42/40 ratio compared to Aβ42 and Aβ40 alone in detecting brain amyloid pathology. 22 , 23 , 24 Further, plasma %P‐tau217 (the ratio of phosphorylated tau217 to non‐phosphorylated tau) has recently been described as superior in the classification of tau‐positron emission tomography (PET) compared to CSF Aβ42/40 25 and to be less affected by kidney dysfunction than p‐tau isoforms alone. 26 Thus, biomarker ratios may enhance the ability to distinguish pathological changes from normal variation.

Thus, we hypothesized that the plasma p‐tau217/BD‐tau ratio reduces the intra‐ and inter‐individual variability compared to p‐tau217 alone. We used data from a previously published AD BBM variability study 3 and performed secondary analyses measuring plasma BD‐tau. We compared intra‐ and inter‐individual variability in plasma p‐tau217 with the p‐tau217/BD‐tau and p‐tau217/T‐tau ratio in memory clinic patients with and without CSF brain amyloidosis. We further tested whether kidney function, glycated hemoglobin, and BMI affected the variability and examined correlations between plasma and CSF markers and peripheral confounders.

2. METHODS

2.1. Study design

This is a test–retest variability study conducted between January 2022 and May 2023, which included patients undergoing CSF analysis for AD biomarkers as part of the routine diagnostic evaluation for suspected neurodegenerative disease in the Memory Clinic at Rigshospitalet, Copenhagen, Denmark. Inclusion criteria were as follows: (1) had undergone lumbar puncture as part of the diagnostic assessment and (2) Mini‐Mental State Examination (MMSE) ≥ 20. Exclusion criteria were (1) stroke within the past 3 months, (2) previous or existing major psychiatric conditions, (3) alcohol or substance abuse within the last 2 years, or (4) participation in intervention studies. Demographic data, medical history, and cognitive test results (MMSE 27 and Addenbrooke's Cognitive Examination [ACE] 28 ) were extracted from medical files from the patients’ diagnostic work‐up program in the clinic. A full description of the study method was published previously. 3

Each participant gave written informed consent prior to enrollment. The study was approved by the Danish Research Ethics Committee (H‐21044863), followed the tenets of the 1975 Helsinki Declaration, and was registered at clinicaltrials.gov (NCT05175664).

2.2. Participants

As part of the diagnostic evaluation, CSF from all 47 participants was analyzed at the Clinical Biochemistry Laboratory at Rigshospitalet. CSF from patients enrolled before June 30, 2022, was analyzed by the Innotest enzyme‐linked immunosorbent assays (ELISA) by Fujirebio, Ghent (n = 8), and CSF collected after July 1, 2022, was analyzed by the Elecsys sandwich electrochemiluminescence immunoassay by Roche, Cobas 8000. For this study, we dichotomized participants according to Aβ status, positive (Aβ+) and negative (Aβ−), based on CSF p‐tau181/Aβ42 ratio cutoff for Innotest: (0.077 [locally established] and for Elecsys [0.023]). 29 , 30 Clinical diagnoses were obtained at a multidisciplinary consensus conference and based on the criteria for AD (mild cognitive impairment [MCI] or dementia), vascular dementia, 31 , 32 , 33 mixed AD and vascular, 33 cerebral amyloid angiopathy, 34 dementia with Lewy bodies, 35 and frontotemporal dementia. 36

RESEARCH IN CONTEXT

  1. Systematic review: The authors reviewed the literature using PudMed and found very few studies investigating the intra‐ and inter‐individual variability of AD plasma biomarkers and how this variability may impact clinical biomarker interpretation.

  2. Interpretation: We found that the short‐term intra‐ and especially inter‐individual variability was lower in the plasma p‐tau217/BD‐tau ratio compared to the variability in plasma p‐tau217 alone.

  3. Future directions: Reduced variability of plasma AD markers is important to ensure relevant clinical interpretation of longitudinal biomarker changes and to prevent misclassification.

2.3. Blood sample collection, processing, and biomarker measurement

A baseline blood sample (V1) was drawn immediately following the lumbar puncture (LP) procedure, via venipuncture in ethylenediaminetetraacetic acid (EDTA)–plasma tubes. The second and third blood samples were collected 4 to 16 days (visit 2 [V2]) and 19 to 36 days (visit 3 [V3]) after V1, respectively. All blood samples were collected between 9:00 a.m. and 3:00 p.m. Fasting status was unknown. CSF from V1 and plasma from V1–V3 were centrifuged at 2000 g, 4°C for 10 min within 30 to 120 min after collection, redistributed into 250‐µL aliquots, and stored at −80°C and shipped to Gothenburg. Analyses of CSF and plasma T‐tau and p‐tau217 were performed from November 2023 to February 2024, and the analyses of BD‐tau were carried out in December 2024 at the Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, University of Gothenburg, Sweden. Separate aliquots of the same blood draw were used for each round of analyses to minimize pre‐analytical variability and avoid additional freeze–thaw cycles. Thus, measurements for all markers were obtained from simultaneously collected specimens. Single‐molecule array (Simoa) technology by Quanterix HD‐X 37 was used to measure T‐tau (Quanterix single plex TAU kit) and p‐tau217 (Quanterix single plex ALZpath p‐tau217 assay). See the detailed description of T‐tau and p‐tau217 measurements elsewhere. 3 Plasma BD‐tau was measured with the previously validated Gothenburg University method. 10 The lower limit of detection (LOD) was 0.044 pg/mL and the dynamic range was 0 to 600pg/mL (for serum/EDTA‐plasma). Both assays demonstrated robust analytical performance with low mean intra‐assay CV (p‐tau217 assay: 5.5%, BD‐tau assay: 9.1%) and mean inter‐assay CV (p‐tau217 assay: 10.5%, BD‐tau assay: 11.7%).

2.4. Statistical analysis

Demographics and baseline characteristics are presented as means with standard deviations. Differences in baseline characteristic variables between Aβ± were examined with two‐sample t‐tests for continuous variables and with Pearson's chi‐squared tests for categorical variables. To accommodate skewed plasma and CSF distribution, log10‐transformation was applied and assessed by visual inspection of histograms and QQ‐plot data. Correlations between plasma and CSF biomarkers, visits, and possible confounders (kidney function, HbA1c, BMI) were examined with Spearman's correlation coefficients.

CVs for intra‐ and inter‐individual variability were examined by fitting linear mixed models (LMMs) with sampling time point as fixed effect and patients as random effects, using the “lmer” function in the lme4 package in R and the “confint” function with the bootstrap method to generate 95% confidence intervals (95% CIs). Intra‐individual CVs were based on the residual's standard deviation, whereas the inter‐individual CVs were calculated from the square root of the total variances, incorporating intra‐individual variability. The method was previously described in detail elsewhere. 3 Assumptions for the linear mixed‐effects models were assessed through diagnostic plots and were adequately met (Figure S1). Absolute biomarker variability was visualized by spaghetti plots, and to account for differing intervals between visits, we calculated standardized per‐day changes in both p‐tau217 and the p‐tau217/BD‐tau ratio (Figure S2).

Models were adjusted separately for estimated glomerular filtration rate (eGFR), hemoglobin A1C (HbA1c), and BMI, added as fixed effects to the model. Further, we tested the combined effect of the covariates, including sex, in a fully adjusted multivariable analysis. Concentrations under the limit of quantification (<LOQ) (T‐tau n = 3, BD‐tau n = 1) were labelled as missing data (NA). Observations labelled NA were removed from the LMMs.

To assess the discriminatory performance of p‐tau217 and the p‐tau217/BD‐tau and p‐tau217/T‐tau ratio at each visit (i.e., their ability to distinguish between Aβ+ and Aβ− individuals), we calculated the area under the receiver operating characteristic curve (AUC) using the pROC package in R. AUCs and corresponding 95% CIs were computed for the full dataset and for complete cases. No train/test data splitting was performed; that is, AUCs were calculated using the entire available dataset at each time point.

All statistical analyses were performed using the R Statistical Software, version 4.4.1, and statistical significance was set to alpha = 0.05 using only two‐tailed tests.

3. RESULTS

3.1. Cohort characteristics

No differences in age, level of cognitive impairment, and distribution of disease severity were found between the Aβ+ and Aβ− groups. The distribution of sex differed between groups, with only two females in the Aβ− group. The Aβ+ group had significantly higher baseline CSF measures of T‐tau and BD‐tau (Table 1). BMI and HbA1c were significantly higher in Aβ− individuals, of which seven patients had type 2 diabetes compared to two patients with type I and II diabetes in the Aβ+ group.

TABLE 1.

Demographics

Aβ+ (n = 29) Aβ− (n = 18) ** p‐value
Age, years mean (SD) 71.51 (5.22) 71.98 (6.52) 0.79 (1)
Sex (men) n (%) 15 (51.7%) 16 (88.9%) 0.01 (2)
MMSE Mean (SD) 26.9 (2.3) 26.8 (2.4) 0.89 (1)
ACE Mean (SD) 79.5 (8.0) 76.7 (11.6) 0.34 (1)
Disease severity (MCI/Mild dementia, n (%)) 16 (55%)/13 (45%) 9/9 (50%) 0.73 (2)
eGFR 81.7 (17.9) 81.6 (23.4) 0.99(2)
HbA1c 38.9 (4.8) 45.1 (13.2) 0.03 * (2)
BMI 24.5 (4.3) 27.5 (5.1) 0.02 * (1)
CSF
p‐tau181/Aβ1‐42 mean (SD) *** 0.05 (0.03) 0.01 (0.004) < 0.0001 * (3)
T‐tau mean (SD) **** 286.7 (191.2) 134.44 (71.2) 0.001 * (3)
BD‐tau mean (SD) pg/mL **** 134.9 (61.5) 73.26 (18.1) 0.00007 * (3)
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N‐missing: AD: CSF n = 4, non‐AD: CSF n = 1. (1) two‐way sample t‐test, (2) Chi‐squared test, (3) T‐test on log‐transformed variables.

*

significant <0.05.

**

In the Aβ‐ group, participants were diagnosed with dementia with Lewy bodies n = 4, frontotemporal dementia n = 2, other tauopathy = 1, cerebral amyloid angiopathy = 1, vascular dementia = 1, normal pressure hydrocephalus = 1, severe kidney disease = 1, head trauma = 1, and unclassified = 6.

***

CSF analyses by Elecsys n = 39 (n = 6 with CSF analysis by Innotest, excluded from this comparison).

****

CSF analyses by Simoa.

Abbreviations: ACE, Addenbrooke's Cognitive Examination; BD‐tau, brain‐derived tau; CSF, cerebrospinal fluid; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; MCI, mild cognitive impairment; MMSE, Mini‐Mental Status Examination; P‐tau, phosphorylated tau; T‐tau, total tau; Aβ, amyloid beta.

Visual inspection of individual trajectories showed less variation for some patients in p‐tau217/BD‐tau compared to p‐tau217 (Figure 1A,B). We observed two patients with high BD‐tau and T‐tau values (Figure S3) and noted that these patients had reduced kidney function (eGFR ≤ 36/min/1,73 m2). The p‐tau217/T‐tau ratio still fluctuated in some Aβ+ patients (Figure S3E). Plasma p‐tau217 and the p‐tau217/BD‐tau group median and interquartile range across visits are visualized in Figure 1C,D.

FIGURE 1.

FIGURE 1

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Plasma levels at each time point: Plasma samples: visit 1 = 45, visit 2 = 43, visit 3 = 40. (A and B) Spaghetti plots with subject‐level p‐tau217 and p‐tau217/BD‐tau changes across three visits. (C and D) Box plots with group median and interquartile range. For better visual inspection, two observations with high plasma values were removed in (B) BD‐tau >1.75pg/mL. See Figure S2 for all observations.

3.2. Biomarker short‐term variability

3.2.1. Intra‐individual variability

The results for intra‐individual variability, adjusted for sampling time point, are presented in Table 2. The lowest intra‐individual variability of the single markers in Aβ+ individuals was observed for BD‐tau, while for Aβ− individuals, the lowest intra‐individual variability was observed in p‐tau217.

TABLE 2.

Intra‐ and inter‐individual variability

Intra‐individual variability CV (%, 95% CI) Inter‐individual variability CV (%, 95% CI)
Amyloid status Model 1: Time‐adjusted Model 2: Fully adjusted Model 1: Time‐adjusted Model 2: Fully adjusted
p‐tau217 Aβ+ * 9.4 (7.4;11.5) * 9.5 (7.6;11.3) 19.1 (15.0;23.4) 13.1 ** (10.5;15.5) *
Aβ− * 5.9 (4.5;7.3) * 6.2 (4.5;7.9) 27.1 (18.4;36.0) 27.7 (16.1;39.5) *
BD‐tau Aβ+ 7.5 (5.97;9.1) 7.5 (6.0;9.1) 12.1 (9.8;14.7) 10.7 (8.4;12.7)
Aβ− 6.6 (5.0;8.2) 6.5 (5.0;8.6) 20.1 (14.1;26.2) 16.8 (10.5;23.3)
T‐tau Aβ+ 10.4 (8.2; 12.5) 10.4 (8.3;12.6) 15.0 (12.1;18.3) 12.7 (10.4;15.1)
Aβ− 11.1 (8.3;14.0) 10.9 (7.8;14.0) 20.6 (14.8;26.7) 19.4 (13.2;26.4)
p‐tau217/BD‐tau Aβ+ 7.1 (5.6;8.4) 7.1 (5.6;8.5) 15.1 (11.7;18.7) 11.1 (8.8;13.7)
Aβ− 7.3 (5.6;9.1) 7.4 (4.5;9.6) 18.4 (13.0;23.8) 18.8 (12.0;26.1)
p‐tau217/T‐tau Aβ+ 13.9 (10.9;16.8) 13.9 (11.2;16.8) 19.3 (15.6;23.1) 18.2 (14.9;21.7)
Aβ− 9.6 (6.9;12.0) 9.5 (6.8;12.2) 30.2 (21.1;39.2) 32.5 (19.8;45.1)
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Note: Intra‐ and inter‐individual variability for each biomarker is presented together with the corresponding confidence interval. Bold highlights how the variability of p‐tau217 is reduced in the ratio and at the group level when adjusted for confounders.

Model 1 is adjusted for sampling time point. Model 2 is adjusted for eGFR, HbA1c, BMI, and sampling time point. Excluding patients with eGFR<<36/min/1,73m2 did not alter the results. Adjusting for sex in model 2 did not affect the variability.

*

Non‐overlapping 95% CI between Aβ+ and Aβ− groups.

**

Non‐overlapping 95% CI between raw (see supplementary Table S1) and Model 2 adjusted for all covariates.

Abbreviations: BD‐tau, brain‐derived tau; CV, coefficient of variation; CI, confidence interval; p‐tau, phosphorylated tau; T‐tau, total tau.

The p‐tau217/BD‐tau ratio showed lower intra‐individual variability in Aβ+ individuals (7.1% [95% CI: 5.6;9.1]) than the intra‐individual variability of p‐tau217 alone (CV 9.4% [95% CI 7.4;11.5]); however, the 95% CIs overlapped.

The p‐tau217/T‐tau ratio showed higher intra‐individual variability (CV 13.9% [95% CI 10.9;16.8]) than p‐tau217 in isolation, and this difference was most pronounced in Aβ+ individuals.

Adjusting for HbA1c, eGFR, BMI, and sampling time point, by addressing the impact of each variable in separate models and by combining all covariates into one model, we observed that adjusting for sampling time point lowered the intra‐individual variability for p‐tau217 and the p‐tau217/T‐tau ratio, in Aβ+ individuals. No other marker's variability was impacted by adjustment. For a full overview of the covariates’ individual impact on the variability (Table S1).

3.2.2. Inter‐individual variability

The lowest inter‐individual variability in the single markers was observed for BD‐tau in the Aβ+ group, showing lower variability compared to p‐tau217 alone (Table 2).

The inter‐individual variability for p‐tau217/BD‐tau was lower in both Aβ± compared with the variability of p‐tau217 alone. p‐tau217/T‐tau showed the highest inter‐individual variability in both Aβ+ and Aβ− and showed higher variability compared with p‐tau217.

Adjusting for all covariates, we observed a lower variability in p‐tau217 (from CV 19.1% [95% CI 15.0;23.4] to CV 13.1% [95% CI 10.5;15.5]) in the Aβ+ group, partially driven by adjustment for BMI; however, the 95% CIs overlapped. This change was not observed in the Aβ− group. Furthermore, we observed a significantly lower variability in the p‐tau217/BD‐tau ratio when adjusting for all covariates (CV 11.1% [95% CI 8.8;13.7]) compared to the crude p‐tau217 variability (CV 19.1% [95% CI 15.0;23.4]). For a full overview of the covariates’ individual impact on the variability, see Table S1.

Results from complete‐case sensitivity analyses were consistent with these findings (Table S2).

3.3. Comparison of AUC performance between visits

Table 3 presents the AUC values for classifying Aβ‐positive and Aβ‐negative individuals (defined by the CSF p‐tau181/Aβ42 ratio) using p‐tau217, p‐tau217/BD‐tau, and p‐tau217/T‐tau. Across V1 and V3, classification performance remained relatively stable for the p‐tau217 and p‐tau217/BD‐tau ratio, while more variability was observed for the p‐tau217/T‐tau ratio. The p‐tau217/BD‐tau ratio showed a numerical improvement in discriminative ability compared to p‐tau217 alone; however, this was not statistically significant. AUC analysis on complete cases did not alter the results (Table S3).

TABLE 3.

Comparison of AUC between visits

V1, n = 45

AUC (95% CI)

V2, n = 43

AUC (95% CI)

V3, n = 40

AUC (95% CI)
p‐tau217 0.76 (0.61;0.92) 0.86 (0.74;1) 0.86 (0.74;1)
p‐tau217/BD‐tau 0.84 (0.72;0.97) 0.91 (0.81;1) 0.93 (0.86;1)
p‐tau217/T‐tau 0.59 (0.40;0.79) 0.75 (0.58;0.9) 0.87 (0.73;1)
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Classification performance for each sampling time point, visit 1 (V1), visit 2 (V2), visit 3 (V3).

Abbreviations: AUC, area under the curve; BD‐tau, brain‐derived tau; CI, confidence interval; p‐tau, phosphorylated tau; T‐tau, total tau.

3.4. Correlations: plasma biomarkers, CSF, and confounders

Plasma p‐tau217 and BD‐tau showed significant and high correlations between each other at all three visits, whereas plasma p‐tau217 and T‐tau showed a moderately significant correlation at V1 and V2, but not at V3 (Figure S3).

There were no significant correlations between plasma BD‐tau and age, MMSE, ACE, BMI, eGFR, or HbA1c. Plasma T‐tau correlated significantly with both eGFR (rho = −0.39, p = 0.008) and HbA1c (rho = −0.38, p = 0.012), but not with MMSE, ACE, or BMI. p‐tau217 was significantly negatively associated with BMI (rho = −0.55, p = 0.0001) (Figure S4).

CSF p‐tau217 correlated significantly with plasma p‐tau217 (rho = 0.37, p = 0.02) and with p‐tau217/BD‐tau (rho = 0.53, p = 0.0005). No correlation was found between CSF p‐tau217 and plasma p‐tau217/T‐tau ratio (rho = 0.28, p = 0.08) (Figure S4).

4. DISCUSSION

In this study, we examined whether the short‐term variability of plasma p‐tau271 could be reduced when combined with the novel plasma marker BD‐tau and whether the variability of the ratio was less affected by biological variables. We found lower intra‐ and inter‐individual variability of p‐tau217/BD‐tau compared to p‐tau217. However, the confidence intervals overlapped, suggesting that the reduction in variability may be modest. For p‐tau217/T‐tau the variability was higher than the variability of p‐tau217, especially the intra‐individual variability in Aβ+ individuals. Adjusting for eGFR, HbA1c, and sampling time point did not change the intra‐individual variability, but we observed reduced inter‐individual variability, especially in the p‐tau217/BD‐tau ratio, in the Aβ+ group.

Assessing single biomarkers, the intra‐individual variability reflects the variation (biological and disease‐related differences in production, release, and clearance) in the biomarker within the individual. Combining biomarkers in ratios, the variability may decrease – if the biomarkers correlate and exhibit the same patterns of variability within the individual (both numerator and denominator fluctuate in the same directions). Hence, the ratio will capture a stable relationship, as we observe in the p‐tau217/BD‐tau ratio. The high variability for p‐tau217/T‐tau ratio in the Aβ+ group compared with p‐tau217 alone could reflect a non‐stable relationship between these markers. Reducing the intra‐individual variability of plasma AD BBMs is important when tracking longitudinal changes of biomarkers levels, that is, in intervention studies or clinical trials.

Inter‐individual variability reflects the variation of a biomarker between individuals within a given group, caused by, e.g. disease heterogeneity, comorbidities, and disease severity. We found that the p‐tau217/BD‐tau ratio exhibited a lower inter‐individual variability compared to p‐tau217 alone, indicating a stable relationship. Adjusting for eGFR, HbA1c, BMI, and sampling time point markedly reduced the variability of p‐tau217 and p‐tau217/BD‐tau in the Aβ+ group, resulting in a halving of the unadjusted variability for p‐tau217 (CV 19.1% [95% CI 15.0;23.4]) compared to the adjusted variability of the p‐tau217/BD‐tau ratio (CV 11.1% [95% CI 8.8;13.7]). Given the importance of correct clinical interpretation of plasma p‐tau217 changes, halving the variability is deemed relevant. We noted that BMI in particular had an impact on p‐tau217 intra‐individual variability, supporting previous findings that BMI influences levels of plasma p‐tau217. 38 , 39 The effect of BMI adjustment on biomarker variability in Aβ+ was not seen in the Aβ− group. While the higher BMI observed in the Aβ− group may suggest an interaction between BMI and Aβ status on plasma p‐tau217, this study design and small cohort size limit causal inference. Alternatively, lower p‐tau217 levels in Aβ− may reduce the observable impact of BMI. We found limited effect of eGFR adjustment, a well‐described peripheral confounder of plasma p‐tau217 levels. 5 , 6 , 26 , 38 This finding could be explained by an overall normal eGFR in this study cohort. A full overview of the individual covariate impact can be found in Table S1. For p‐tau217/T‐tau the inter‐individual variability was higher than the variability of p‐tau217 alone, especially in Aβ+ individuals, which could reflect biomarkers fluctuating in different directions. We did not observe a relevant reduction of variability in T‐tau or the p‐tau217/T‐tau ratio when adjusting for covariates. Low inter‐individual variability, meaning patients within the same disease group are likely to exhibit similar biomarker levels, is preferable as it allows for more consistent interpretations across individuals. This should be distinguished from a preferred wide dynamic range, which captures a variety of values, enabling differentiation across, that is, disease stages or conditions.

CSF p‐tau217 correlated significantly with plasma p‐tau217, but a higher correlation was observed with the p‐tau217/BD‐tau ratio. The significant correlations found between plasma T‐tau and HbA1c and eGFR indicate that plasma T‐tau levels are affected by peripheral factors. The finding that these confounders were not correlated with plasma BD‐tau shows that plasma BD‐tau is less likely to be affected by these peripheral factors. We observed a few patients with moderate to severe reduced kidney function eGFR<36/min/1.73m2 and noted high plasma markers in these patients. This is in line with a recently published paper concluding that eGFR > 60 has a limited effect on AD BBM, but caution should be taken when eGFR <60/min/1.73 m.2 5 However, excluding these patients or adjusting for eGFR did not alter our results in this study. Future research should study the impact of moderate to severe reduced kidney function on AD blood biomarkers in clinical perspectives.

Classification performance remained relatively stable for the p‐tau217 and p‐tau217/BD‐tau ratio, while more variability was observed for the p‐tau217/T‐tau ratio. It is important to note that the classification analyses aimed to explore potential changes in performance between visits, rather than to provide estimates of diagnostic accuracy for clinical application. The study was not powered for formal diagnostic validation, and our modest sample size in this regard limited the possibility of performing internal validation procedures, such as cross‐validation. However, sensitivity analyses based on complete‐case data yielded similar AUC results, suggesting that missing data did not substantially impact our findings. We acknowledge that assessing the variability of the p‐tau217/BD‐tau ratio may be premature until its clinical relevance is more clearly established.

This study has some limitations, such as the modest sample size. There was a notable sex imbalance between the Aβ+ and Aβ− groups, which may impact the generalizability of the findings. Sex was included in the fully adjusted multivariable model to mitigate confounding, and though we did not observe significant changes in the variability, the uneven distribution limited interpretation of sex‐specific effects. Nevertheless, the two groups were well characterized, and the 1‐month timeframe for sample collection was considered appropriate to minimize the potential influence of disease progression on biomarker levels, given the chronic nature of neurodegenerative diseases. Blood collection was performed without information on last‐food intake, and though studies have suggested significant fluctuations due to food intake, 40 we consider potential postprandial differences as part of the observed short‐term variability this study was designed to investigate. While circadian rhythms may influence plasma levels of AD biomarkers, 41 evidence suggests that plasma p‐tau217 may not exhibit substantial diurnal variability. 42 To limit potential circadian effects and variability from sampling intervals, blood sampling was restricted to daytime hours, consistent with clinical practice, and sampling time points were included in all models. However, exact sampling times were not recorded, and circadian influence cannot be entirely ruled out. Our findings should be replicated in larger, more demographically balanced cohorts. Future studies should focus on demonstrating the clinical utility of the p‐tau217/BD‐tau ratio and evaluating the performance of the p‐tau217/BD‐tau ratio at predicting or tracking disease progression in AD patients.

In conclusion, the reduced variability of the plasma p‐tau217/BD‐tau ratio, adjusted for various confounders, is promising to ensure relevant clinical interpretation of longitudinal biomarker changes and to prevent misclassification. We suggest that the p‐tau217/BD‐tau ratio should be considered in instances where low variability is important and, further, to consider the influence of peripheral confounders, especially BMI.

CONFLICT OF INTEREST STATEMENT

A.H.S. has served as a consultant for Eisai/BioArctic (remuneration paid to the institution).

K.B. has served as a consultant and on advisory boards for AbbVie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served on data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials, and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai, and Roche Diagnostics; and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper.

H.Z. has served at scientific advisory boards and/or as a consultant for AbbVie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave; has given lectures at symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche; and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work).

K.F. serves or has served on a scientific advisory board and as scientific consultant for Novo Nordisk, Roche Diagnostics, and Eisai; has given lectures at symposia for Novo Nordisk, Eli Lilly, and Eisai/BioArctic (renumeration paid to institution); and has served or serves as principal investigator in trials for Roche, Biogen, AbbVie, Novo Nordisk, and Roche Diagnostics (remuneration paid to institution). K.F. also serves as editor‐in‐chief for Alzheimer´s Research and Therapy (Springer) for which personal remuneration is paid.

The other authors have no conflict of interest to report.

CONSENT STATEMENT

All participants gave written informed consent prior to enrollment.

Supporting information

Supporting Information

TRC2-11-e70143-s002.pdf (975KB, pdf)

Supporting Information

TRC2-11-e70143-s001.docx (1.3MB, docx)

ACKNOWLEDGMENTS

The authors wish to thank all participants and relatives without whom this study would not have been possible. The authors also wish to thank Theis Mouritsen and Jette Pedersen from the Danish Dementia Biobank. Funding for this project was received from Alzheimerforeningens Forskningsfond, A.P. Møller Foundation, and Direktør Emil C. Hertz og Hustru Inger Hertz' Fond. The salary of FKC was supported by Michaelsen Foundation, M.L. Jørgensen og Gunnar Hansens Foundation, Rigshospitalets Forskningspulje, and the Danish Ministry of Health. AHS and the Danish Dementia Biobank were supported by the Absalon Foundation of May 1, 1978.K.B. is supported by the Swedish Research Council (2017‐00915 and 2022‐00732), the Swedish Alzheimer Foundation (AF‐930351, AF‐939721, AF‐968270, and AF‐994551), Hjärnfonden, Sweden (FO2017‐0243 and ALZ2022‐0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF‐agreement (ALFGBG‐715986 and ALFGBG‐965240), the European Union Joint Program for Neurodegenerative Disorders (JPND2019‐466‐236), the Alzheimer's Association 2021 Zenith Award (ZEN‐21‐848495), the Alzheimer's Association 2022‐2025 grant (SG‐23‐1038904 QC), La Fondation Recherche Alzheimer (FRA), Paris, France, the Kirsten and Freddy Johansen Foundation, Copenhagen, Denmark, and Familjen Rönströms Stiftelse, Stockholm, Sweden. H.Z. is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (2023‐00356, 2022‐01018, and 2019‐02397), the European Union's Horizon Europe Research and Innovation Programme under grant agreement No. 101053962, Swedish State Support for Clinical Research (ALFGBG‐71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (201809‐2016862), the AD Strategic Fund and the Alzheimer's Association (ADSF‐21‐831376‐C, ADSF‐21‐831381‐C, ADSF‐21‐831377‐C, and ADSF‐24‐1284328‐C), the European Partnership on Metrology, co‐financed by the European Union's Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand, 22HLT07), the Bluefield Project, Cure Alzheimer's Fund, the Olav Thon Foundation, the Erling‐Persson Family Foundation, Familjen Rönströms Stiftelse, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (FO2022‐0270), the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska‐Curie Grant Agreement No. 860197 (MIRIADE), the European Union Joint Programme—Neurodegenerative Disease Research (JPND2021‐00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI‐1003). The funders had no role in the design or reporting of the study.

Clemmensen FK, Gonzalez‐Ortiz F, Gramkow MH, et al. The plasma p‐tau217/BD‐tau ratio improves biomarker short‐term variability in memory clinic patients. Alzheimer's Dement. 2025;11:e70143. 10.1002/trc2.70143

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Supplementary Materials

Supporting Information

TRC2-11-e70143-s002.pdf (975KB, pdf)

Supporting Information

TRC2-11-e70143-s001.docx (1.3MB, docx)

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